How to add my own dataset to MobileNet model?

Question

I am trying to do prediction by loading my own dataset to the MobileNet model.According to this tutorial dataset(cats_vs_dogs) is loaded from the TensorFlow dataset as shown bellow

splits = tfds.Split.ALL.subsplit(weighted=(80, 20))

splits, info = tfds.load('cats_vs_dogs', with_info=True, as_supervised=True, split = splits)

(train_examples, validation_examples) = splits

num_examples = info.splits['train'].num_examples
num_classes = info.features['label'].num_classes

How to change above code to load my own cat vs dog dataset ?

bsquare · Answer 1 · 2020-04-16T06:30:08.060

Please replace above 5 lines of code with below code to read your own data and rest other code should be same as per your network.

#Connect to dataset folders
train_dir  = '/content/drive/My Drive/Dogs_Vs_Cats/train'
test_dir = '/content/drive/My Drive/Dogs_Vs_Cats/test'

#Create data generator for training and testing
train_datagen = ImageDataGenerator(**datagen_kwargs)
test_datagen = ImageDataGenerator(**datagen_kwargs)

train_data = train_datagen.flow_from_directory(
    train_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')

test_data = test_datagen.flow_from_directory(
    test_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')

Please refer end to end implementation of using own data set in below:

%tensorflow_version 1.x   
import os
import numpy as np
from keras import layers
import pandas as pd
from tensorflow.keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from tensorflow.keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras import regularizers, optimizers
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator

import keras.backend as K

K.set_image_data_format('channels_last')

from google.colab import drive
drive.mount('/content/drive')

train_dir  = '/content/drive/My Drive/Dogs_Vs_Cats/train'
test_dir = '/content/drive/My Drive/Dogs_Vs_Cats/test'

img_width, img_height = 300, 281
input_shape = img_width, img_height, 3

train_samples = 2000
test_samples = 1000
epochs = 30
batch_size = 32

train_datagen = ImageDataGenerator(
    rescale = 1. /255,
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True)

test_datagen = ImageDataGenerator(
    rescale = 1. /255)

train_data = train_datagen.flow_from_directory(
    train_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')

test_data = test_datagen.flow_from_directory(
    test_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')


model = Sequential()

model.add(Conv2D(32, (7, 7), strides = (1, 1), input_shape = input_shape))
model.add(BatchNormalization(axis = 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (7, 7), strides = (1, 1)))
model.add(BatchNormalization(axis = 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss = 'binary_crossentropy',
            optimizer = 'rmsprop',
            metrics = ['accuracy'])

model.build(input_shape)
model.summary() 


model.fit_generator(
        train_data,
        steps_per_epoch = train_samples//batch_size,
        epochs = epochs,
        validation_data = test_data,
        verbose = 1,
        validation_steps = test_samples//batch_size)

How to add my own dataset to MobileNet model?

1 Answers1